{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-26T03:34:58.132163Z",
     "start_time": "2024-09-26T03:34:58.128229Z"
    }
   },
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    " \n"
   ],
   "outputs": [],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:05.780086Z",
     "start_time": "2024-09-26T03:34:58.161666Z"
    }
   },
   "cell_type": "code",
   "source": [
    "######################################数据的合并#########################################\n",
    "# 训练集\n",
    "train_LogInfo = pd.read_csv('./PPD_LogInfo_3_1_Training_Set.csv',encoding='gbk')\n",
    "train_Master = pd.read_csv('./PPD_Training_Master_GBK_3_1_Training_Set.csv',encoding='gbk')\n",
    "train_Userupdate = pd.read_csv('./PPD_Userupdate_Info_3_1_Training_Set.csv',encoding='gbk')\n",
    " \n",
    "#  测试集\n",
    "test_LogInfo = pd.read_csv('./PPD_LogInfo_2_Test_Set.csv',encoding='gbk')\n",
    "test_Master = pd.read_csv('./PPD_Master_GBK_2_Test_Set.csv',encoding='gb18030')\n",
    "test_Userupdate = pd.read_csv('./PPD_Userupdate_Info_2_Test_Set.csv',encoding='gbk')\n",
    " \n",
    "# 合并时用于标记哪些样本来自训练集和测试集\n",
    "train_Master['sample_status']='train'\n",
    "test_Master['sample_status']='test'\n",
    " \n",
    "# 训练集和测试集的合并(axis=0,增加行）\n",
    "df_Master = pd.concat([train_Master,test_Master],axis=0).reset_index(drop=True)\n",
    "df_LogInfo=pd.concat([train_LogInfo,test_LogInfo],axis=0).reset_index(drop=True)\n",
    "df_Userupdate=pd.concat([train_Userupdate,test_Userupdate],axis=0).reset_index(drop=True)\n",
    " \n",
    "df_Master.to_csv(\"./拍拍贷“魔镜杯”风控初赛数据/df_Master.csv\",encoding='gb18030',index=False)\n",
    "df_LogInfo.to_csv(\"./拍拍贷“魔镜杯”风控初赛数据/df_LogInfo.csv\",encoding='gb18030',index=False)\n",
    "df_Userupdate.to_csv(\"./拍拍贷“魔镜杯”风控初赛数据/df_Userupdate.csv\",encoding='gb18030',index=False)\n"
   ],
   "id": "d978c20425b89d0d",
   "outputs": [],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:07.627520Z",
     "start_time": "2024-09-26T03:35:05.782079Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#####################################数据的探索行分析#####################################\n",
    "# 导入合并后的数据\n",
    "df_Master = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/df_Master.csv',encoding='gb18030')\n",
    "df_LogInfo = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/df_LogInfo.csv',encoding='gb18030')\n",
    "df_Userupdate = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/df_Userupdate.csv',encoding='gb18030')\n",
    " \n",
    "# 定义显示形式\n",
    "pd.set_option(\"display.max_columns\",len(train_Master.columns)) \n",
    "df_Master.head(20)\n",
    "# 可以看到的是，数据主要分为：\n",
    "# 教育信息、第三方信息、社交网络信息、用户信息、网络博客信息、目标标签（target)和sample_status(自定义，用于区分数据来源于测试/训练集)\n",
    " \n",
    "# 察看训练集中好坏样本比例，1为坏样本\n",
    "df_Master.target.value_counts()\n",
    " \n",
    "# 每个个体都是独一的\n",
    "len(np.unique(df_Master.Idx))\n",
    "# df_Master.shape  # (49999, 229)"
   ],
   "id": "66d1da29a67ab87a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49999"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:08.683691Z",
     "start_time": "2024-09-26T03:35:07.628433Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#######################################（1）缺失值处理###################################\n",
    "# 原始中大量的缺失值用-1标识，我们将其替换成np.nan\n",
    "df_Master = df_Master.replace({-1:np.nan})\n",
    "df_Master.head(15)\n",
    " \n",
    "# 缺失值的可视化——白色越多，代表变量缺失越多\n",
    "# import missingno as msno\n",
    "# %matplotlib inline\n",
    "# msno.bar(df_Master)\n",
    " \n",
    "# 缺失占比超过80%的变量列表\n",
    "missing_columns=[]\n",
    "for column in df_Master.columns:\n",
    "    if sum(pd.isnull(df_Master[column]))/len(df_Master)>=0.8:\n",
    "        missing_columns.append(column)\n",
    "print(len(missing_columns))\n",
    "print(missing_columns)\n"
   ],
   "id": "bfde02f8c2729394",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "['WeblogInfo_1', 'WeblogInfo_3', 'ThirdParty_Info_Period7_1', 'ThirdParty_Info_Period7_2', 'ThirdParty_Info_Period7_3', 'ThirdParty_Info_Period7_4', 'ThirdParty_Info_Period7_5', 'ThirdParty_Info_Period7_6', 'ThirdParty_Info_Period7_7', 'ThirdParty_Info_Period7_8', 'ThirdParty_Info_Period7_9', 'ThirdParty_Info_Period7_10', 'ThirdParty_Info_Period7_11', 'ThirdParty_Info_Period7_12', 'ThirdParty_Info_Period7_13', 'ThirdParty_Info_Period7_14', 'ThirdParty_Info_Period7_15', 'ThirdParty_Info_Period7_16', 'ThirdParty_Info_Period7_17', 'SocialNetwork_3', 'SocialNetwork_4', 'SocialNetwork_5', 'SocialNetwork_6', 'SocialNetwork_7', 'SocialNetwork_11']\n"
     ]
    }
   ],
   "execution_count": 54
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:19.513874Z",
     "start_time": "2024-09-26T03:35:08.684685Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 筛掉缺失大于80%的变量\n",
    "df_Master = df_Master.loc[:,list(~df_Master.columns.isin(missing_columns))]\n",
    "df_Master.shape\n",
    " \n",
    "# 再来看样本的特征缺失（行缺失）\n",
    "# 对于某个样本，特征缺失大于100\n",
    "missing_index=[]\n",
    "for i in np.arange(df_Master.shape[0]):\n",
    "    if list(df_Master.loc[i,:].isnull()).count(True)>100:\n",
    "        missing_index.append(i)\n",
    "print(missing_index)\n"
   ],
   "id": "9c488189fd5d48a6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 105, 963, 1036, 1630, 2187, 2464, 2606, 2660, 2675, 2707, 2769, 3093, 3145, 3326, 3387, 3643, 3865, 3910, 3924, 3991, 3993, 4096, 4112, 4145, 4164, 4243, 4521, 4674, 4688, 4703, 4890, 4900, 5001, 5019, 5340, 5358, 5381, 5417, 5668, 5933, 5939, 6148, 6331, 6385, 6418, 6710, 6754, 6759, 6805, 6845, 6880, 6885, 6902, 6928, 6958, 7042, 7184, 7201, 7249, 7300, 7404, 7424, 7452, 7477, 7510, 7730, 7799, 7832, 8026, 8059, 8135, 8639, 8696, 8705, 9033, 9131, 9181, 9395, 9412, 9423, 9602, 9974, 10109, 10119, 10150, 10493, 10543, 10627, 10709, 10742, 11300, 11367, 11413, 11536, 11562, 11901, 11953, 12205, 12527, 12630, 13001, 13074, 13266, 13488, 13551, 13712, 13744, 13955, 14406, 14455, 14943, 15123, 15251, 15336, 15381, 15785, 16254, 16267, 16340, 16639, 16765, 16826, 16932, 17950, 18732, 18910, 19072, 19130, 19339, 19597, 19715, 19868, 19950, 20182, 20436, 20647, 20842, 21857, 21978, 22042, 22380, 22427, 22609, 22666, 22898, 22924, 24526, 24643, 24846, 26066, 26131, 26143, 26326, 26329, 26498, 27241, 27463, 27482, 27644, 27771, 28248, 28401, 28506, 28708, 29718, 29757, 29886, 30686, 30812, 30957, 31189, 31359, 31661, 31766, 31847, 31864, 32130, 32464, 32493, 32515, 32589, 32638, 32772, 32858, 32995, 33012, 33054, 33144, 33410, 33439, 33460, 33474, 33547, 33675, 33684, 33734, 33750, 33851, 33909, 33944, 34064, 34087, 34114, 34390, 34492, 34606, 34720, 34821, 34901, 34924, 34990, 35174, 35504, 35550, 35623, 35726, 35755, 35834, 36016, 36127, 36131, 36163, 36330, 36534, 37388, 38020, 38031, 38109, 38210, 38276, 38406, 38666, 39147, 39204, 39474, 39674, 39682, 39743, 39783, 39848, 40000, 40060, 40112, 40155, 40157, 40685, 40758, 40790, 40841, 41002, 41055, 41768, 41786, 42197, 42666, 43156, 43496, 43514, 43549, 43701, 44031, 44123, 44279, 44299, 44549, 44671, 44740, 44891, 45374, 45417, 45948, 46320, 46838, 47326, 47569, 47572, 47649, 47734, 48035, 48191, 48192, 48473, 48498, 48674, 48857, 48863, 49142, 49725, 49787, 49790, 49860, 49866, 49897, 49899, 49904, 49970, 49991]\n"
     ]
    }
   ],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:19.991825Z",
     "start_time": "2024-09-26T03:35:19.515867Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 删除特征缺失超过100的行\n",
    "df_Master = df_Master.drop(missing_index).reset_index(drop=True)\n",
    "df_Master.shape\n",
    " \n",
    "# 单变量占比分析\n",
    "print(\"原变量总数：\",'\\n',len(df_Master.columns))\n",
    "cols = [col for col in df_Master.columns if col not in ('target','sample_status')]\n",
    "print(\"排除目标标签和标记训练集和测试集来源的变量总数：\",'\\n',len(cols))\n",
    " \n",
    " \n",
    "# 某个变量的某个取值占比超过90%，说明信息含量低，可以删除\n",
    "drop_cols_simple=[]\n",
    "for col in cols:\n",
    "    if max(df_Master[col].value_counts())/len(df_Master)>0.9:\n",
    "        drop_cols_simple.append(col)\n",
    "print(drop_cols_simple)\n",
    "print(len(drop_cols_simple))\n",
    " \n",
    "df_Master = df_Master.drop(drop_cols_simple,axis=1)\n",
    "df_Master.shape\n",
    "df_Master = df_Master.reset_index(drop=True)\n",
    " \n",
    "# 剩下的变量的类型\n",
    "df_Master.dtypes.value_counts()\n",
    " \n",
    "objectcol = df_Master.select_dtypes(include=[\"object\"]).columns\n",
    "numcol = df_Master.select_dtypes(include=[np.float64]).columns\n",
    " \n",
    "# 分类型变量只有12个，我们来看一下这些变量有什么规律\n",
    "df_Master[objectcol]\n",
    " \n",
    " "
   ],
   "id": "c8e567aca3026f7b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原变量总数： \n",
      " 204\n",
      "排除目标标签和标记训练集和测试集来源的变量总数： \n",
      " 202\n",
      "['WeblogInfo_9', 'WeblogInfo_10', 'WeblogInfo_11', 'WeblogInfo_12', 'WeblogInfo_13', 'WeblogInfo_14', 'UserInfo_21', 'UserInfo_22', 'UserInfo_23', 'UserInfo_24', 'Education_Info1', 'Education_Info2', 'Education_Info3', 'Education_Info4', 'Education_Info5', 'Education_Info6', 'Education_Info7', 'Education_Info8', 'WeblogInfo_23', 'WeblogInfo_25', 'WeblogInfo_26', 'WeblogInfo_28', 'WeblogInfo_29', 'WeblogInfo_31', 'WeblogInfo_32', 'WeblogInfo_34', 'WeblogInfo_35', 'WeblogInfo_37', 'WeblogInfo_38', 'WeblogInfo_39', 'WeblogInfo_40', 'WeblogInfo_41', 'WeblogInfo_42', 'WeblogInfo_43', 'WeblogInfo_44', 'WeblogInfo_45', 'WeblogInfo_46', 'WeblogInfo_47', 'WeblogInfo_48', 'WeblogInfo_49', 'WeblogInfo_50', 'WeblogInfo_51', 'WeblogInfo_52', 'WeblogInfo_53', 'WeblogInfo_54', 'WeblogInfo_55', 'WeblogInfo_56', 'WeblogInfo_57', 'WeblogInfo_58', 'SocialNetwork_1', 'SocialNetwork_2', 'SocialNetwork_14', 'SocialNetwork_15', 'SocialNetwork_16']\n",
      "54\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      UserInfo_2 UserInfo_4 UserInfo_7 UserInfo_8 UserInfo_9 UserInfo_19  \\\n",
       "0             深圳         深圳         广东         深圳      中国移动          四川省   \n",
       "1             温州         温州         浙江         温州      中国移动          福建省   \n",
       "2             宜昌         宜昌         湖北         宜昌      中国电信          湖北省   \n",
       "3             南平         南平         福建         南平      中国移动          江西省   \n",
       "4             辽阳         辽阳         辽宁         辽阳      中国移动          辽宁省   \n",
       "...          ...        ...        ...        ...        ...         ...   \n",
       "49696       鄂尔多斯       鄂尔多斯        内蒙古       鄂尔多斯      中国联通       内蒙古自治区   \n",
       "49697         聊城         聊城         山东         聊城      中国移动          山东省   \n",
       "49698        秦皇岛        秦皇岛         河北        秦皇岛      中国移动          河北省   \n",
       "49699         广州         茂名         广东         深圳      中国联通          广东省   \n",
       "49700         上海         常州         不详         不详         不详         江西省   \n",
       "\n",
       "      UserInfo_20 WeblogInfo_19 WeblogInfo_20 WeblogInfo_21 ListingInfo  \\\n",
       "0             南充市             I            I5             D    2014/3/5   \n",
       "1              不详             I            I5             D   2014/2/26   \n",
       "2             宜昌市             I            I5             D   2014/2/28   \n",
       "3              不详             I            I5             D   2014/2/25   \n",
       "4             锦州市             I           NaN             D   2014/2/27   \n",
       "...           ...           ...           ...           ...         ...   \n",
       "49696          不详             I            I4             D   26/2/2014   \n",
       "49697          不详             I            I5             D   27/2/2014   \n",
       "49698        秦皇岛市             I            I5             D  16/11/2013   \n",
       "49699         茂名市             I            I5             D   21/2/2014   \n",
       "49700          不详             I            I5             D   28/2/2014   \n",
       "\n",
       "      sample_status  \n",
       "0             train  \n",
       "1             train  \n",
       "2             train  \n",
       "3             train  \n",
       "4             train  \n",
       "...             ...  \n",
       "49696          test  \n",
       "49697          test  \n",
       "49698          test  \n",
       "49699          test  \n",
       "49700          test  \n",
       "\n",
       "[49701 rows x 12 columns]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserInfo_2</th>\n",
       "      <th>UserInfo_4</th>\n",
       "      <th>UserInfo_7</th>\n",
       "      <th>UserInfo_8</th>\n",
       "      <th>UserInfo_9</th>\n",
       "      <th>UserInfo_19</th>\n",
       "      <th>UserInfo_20</th>\n",
       "      <th>WeblogInfo_19</th>\n",
       "      <th>WeblogInfo_20</th>\n",
       "      <th>WeblogInfo_21</th>\n",
       "      <th>ListingInfo</th>\n",
       "      <th>sample_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深圳</td>\n",
       "      <td>深圳</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>四川省</td>\n",
       "      <td>南充市</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/3/5</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>温州</td>\n",
       "      <td>温州</td>\n",
       "      <td>浙江</td>\n",
       "      <td>温州</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>福建省</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/26</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>宜昌</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>湖北</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>中国电信</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>宜昌市</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/28</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>南平</td>\n",
       "      <td>南平</td>\n",
       "      <td>福建</td>\n",
       "      <td>南平</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>江西省</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/25</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>辽阳</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>辽宁省</td>\n",
       "      <td>锦州市</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/27</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49696</th>\n",
       "      <td>鄂尔多斯</td>\n",
       "      <td>鄂尔多斯</td>\n",
       "      <td>内蒙古</td>\n",
       "      <td>鄂尔多斯</td>\n",
       "      <td>中国联通</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I4</td>\n",
       "      <td>D</td>\n",
       "      <td>26/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49697</th>\n",
       "      <td>聊城</td>\n",
       "      <td>聊城</td>\n",
       "      <td>山东</td>\n",
       "      <td>聊城</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>山东省</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>27/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49698</th>\n",
       "      <td>秦皇岛</td>\n",
       "      <td>秦皇岛</td>\n",
       "      <td>河北</td>\n",
       "      <td>秦皇岛</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>河北省</td>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>16/11/2013</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49699</th>\n",
       "      <td>广州</td>\n",
       "      <td>茂名</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>中国联通</td>\n",
       "      <td>广东省</td>\n",
       "      <td>茂名市</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>21/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49700</th>\n",
       "      <td>上海</td>\n",
       "      <td>常州</td>\n",
       "      <td>不详</td>\n",
       "      <td>不详</td>\n",
       "      <td>不详</td>\n",
       "      <td>江西省</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>28/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49701 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:20.016386Z",
     "start_time": "2024-09-26T03:35:19.992815Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可以看到的是\n",
    "# 表示省份的有\n",
    "# UserInfo_19和UserInfo_7\n",
    "# 表示城市的有\n",
    "# UserInfo_2,UserInfo_20,UserInfo_4,UserInfo_8\n",
    "city_feature = ['UserInfo_2','UserInfo_20','UserInfo_4','UserInfo_8']\n",
    "province_feature=['UserInfo_7','UserInfo_19']\n",
    " \n",
    "print(\"城市特征：\")\n",
    "for col in city_feature:\n",
    "    print(col,\":\",df_Master[col].nunique())\n",
    " \n",
    "print('\\n')\n",
    "print(\"省份特征：\")\n",
    "for col in province_feature:\n",
    "        print(col,\":\",df_Master[col].nunique())"
   ],
   "id": "17103951978c10be",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "城市特征：\n",
      "UserInfo_2 : 329\n",
      "UserInfo_20 : 307\n",
      "UserInfo_4 : 332\n",
      "UserInfo_8 : 664\n",
      "\n",
      "\n",
      "省份特征：\n",
      "UserInfo_7 : 32\n",
      "UserInfo_19 : 31\n"
     ]
    }
   ],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:20.023961Z",
     "start_time": "2024-09-26T03:35:20.017349Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(df_Master.UserInfo_8.unique()[:50])\n",
    "# 可以看到，同一个城市表达不一"
   ],
   "id": "69b07291fba74491",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['深圳' '温州' '宜昌' '南平' '辽阳' '不详' '包头' '赤峰' '鄂州' '武汉' '长沙' '漳州' '牡丹江' '太原市'\n",
      " '北京' '忻州' '三明' '临沂' '福州' '泰州市' '大同' '红河' '郴州' '常州' '湖州' '佛山' '天津' '南宁'\n",
      " '聊城' '柳州' '广州市' '太原' '重庆' '杭州' '景德镇' '上饶' '鸡西' '资阳' '成都' '济宁' '滨州' '渭南'\n",
      " '广州' '都匀' '廊坊' '西宁市' '金华' '龙岩' '清远' '兰州']\n"
     ]
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:20.072028Z",
     "start_time": "2024-09-26T03:35:20.024955Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 去掉字段中的“市”，保持统一\n",
    "df_Master['UserInfo_8'] = [a[:-1] if a.find('市')!= -1 else a[:] for a in df_Master['UserInfo_8']]\n",
    " \n",
    "# 清理后非重复计数减小\n",
    "df_Master['UserInfo_8'].nunique()\n",
    " \n",
    " \n",
    "# 再来看看数值型变量\n",
    "df_Master[numcol].head(20)\n",
    "# 这里我们不对数值变量进行缺失值插值或者填充，直接用于后期建模\n",
    " \n",
    "# 再来看看其他的表——该表显示了客户修改信息的日志\n",
    "df_Userupdate"
   ],
   "id": "778e00cb7e5e5932",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          Idx ListingInfo1    UserupdateInfo1 UserupdateInfo2\n",
       "0       10001   2014/03/05       _EducationId      2014/02/20\n",
       "1       10001   2014/03/05         _HasBuyCar      2014/02/20\n",
       "2       10001   2014/03/05    _LastUpdateDate      2014/02/20\n",
       "3       10001   2014/03/05  _MarriageStatusId      2014/02/20\n",
       "4       10001   2014/03/05       _MobilePhone      2014/02/20\n",
       "...       ...          ...                ...             ...\n",
       "621290   9994   2014/02/28                _QQ      2014/02/20\n",
       "621291   9994   2014/02/28  _ResidenceAddress      2014/02/20\n",
       "621292   9994   2014/02/28    _ResidencePhone      2014/02/20\n",
       "621293   9994   2014/02/28   _ResidenceTypeId      2014/02/20\n",
       "621294   9994   2014/02/28    _ResidenceYears      2014/02/20\n",
       "\n",
       "[621295 rows x 4 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>ListingInfo1</th>\n",
       "      <th>UserupdateInfo1</th>\n",
       "      <th>UserupdateInfo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_EducationId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_HasBuyCar</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_LastUpdateDate</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MarriageStatusId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621290</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_QQ</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621291</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceAddress</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621292</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidencePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621293</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceTypeId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621294</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceYears</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>621295 rows × 4 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 59
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:20.216326Z",
     "start_time": "2024-09-26T03:35:20.073025Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将上表的大小写进行统一\n",
    "df_Userupdate['UserupdateInfo1'] = df_Userupdate.UserupdateInfo1.map(lambda s:s.lower())\n",
    " \n",
    " \n",
    "######################################特征工程阶段#######################################\n",
    "# 至此，我们进入特征处理阶段\n",
    "# 首先对类别变量进行变换\n",
    "df_Master[objectcol]\n",
    " \n",
    " \n",
    "# 1)省份特征————————推测可能一个是籍贯省份，一个是居住省份\n",
    "# 首先看看各省份好坏样本的分布占比\n",
    "def get_badrate(df,col):\n",
    "    '''\n",
    "    根据某个变量计算违约率\n",
    "    '''\n",
    "    group = df.groupby(col)\n",
    "    df=pd.DataFrame()\n",
    "    df['total'] = group.target.count()\n",
    "    df['bad'] = group.target.sum()\n",
    "    df['badrate'] = round(df['bad']/df['total'],4)*100  # 百分比形式\n",
    "    return df.sort_values('badrate',ascending=False)\n",
    " \n",
    "# 户籍省份的违约率计算\n",
    "province_original = get_badrate(df_Master,'UserInfo_19')\n",
    "province_original "
   ],
   "id": "a863b1d3ec6745d8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             total    bad  badrate\n",
       "UserInfo_19                       \n",
       "天津市            137   17.0    12.41\n",
       "山东省           2366  256.0    10.82\n",
       "吉林省            498   47.0     9.44\n",
       "黑龙江省           813   71.0     8.73\n",
       "湖南省           1753  149.0     8.50\n",
       "辽宁省            683   58.0     8.49\n",
       "四川省           1833  155.0     8.46\n",
       "湖北省           1635  132.0     8.07\n",
       "海南省            162   13.0     8.02\n",
       "河北省           1202   96.0     7.99\n",
       "江苏省           1817  145.0     7.98\n",
       "安徽省           1452  111.0     7.64\n",
       "重庆市            389   29.0     7.46\n",
       "陕西省            691   49.0     7.09\n",
       "河南省           1806  126.0     6.98\n",
       "上海市            189   13.0     6.88\n",
       "贵州省            663   45.0     6.79\n",
       "江西省           1146   77.0     6.72\n",
       "广东省           2393  160.0     6.69\n",
       "山西省            942   57.0     6.05\n",
       "福建省           2009  110.0     5.48\n",
       "甘肃省            535   29.0     5.42\n",
       "广西壮族自治区       1195   64.0     5.36\n",
       "浙江省           1677   86.0     5.13\n",
       "云南省            678   34.0     5.01\n",
       "内蒙古自治区         567   28.0     4.94\n",
       "北京市            128    6.0     4.69\n",
       "宁夏回族自治区        202    9.0     4.46\n",
       "青海省             68    3.0     4.41\n",
       "新疆维吾尔自治区       202    7.0     3.47\n",
       "西藏自治区            1    0.0     0.00"
      ],
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>bad</th>\n",
       "      <th>badrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserInfo_19</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>137</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>2366</td>\n",
       "      <td>256.0</td>\n",
       "      <td>10.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>498</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>813</td>\n",
       "      <td>71.0</td>\n",
       "      <td>8.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>1753</td>\n",
       "      <td>149.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁省</th>\n",
       "      <td>683</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川省</th>\n",
       "      <td>1833</td>\n",
       "      <td>155.0</td>\n",
       "      <td>8.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北省</th>\n",
       "      <td>1635</td>\n",
       "      <td>132.0</td>\n",
       "      <td>8.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南省</th>\n",
       "      <td>162</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北省</th>\n",
       "      <td>1202</td>\n",
       "      <td>96.0</td>\n",
       "      <td>7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏省</th>\n",
       "      <td>1817</td>\n",
       "      <td>145.0</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽省</th>\n",
       "      <td>1452</td>\n",
       "      <td>111.0</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆市</th>\n",
       "      <td>389</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西省</th>\n",
       "      <td>691</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南省</th>\n",
       "      <td>1806</td>\n",
       "      <td>126.0</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海市</th>\n",
       "      <td>189</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州省</th>\n",
       "      <td>663</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西省</th>\n",
       "      <td>1146</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东省</th>\n",
       "      <td>2393</td>\n",
       "      <td>160.0</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西省</th>\n",
       "      <td>942</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建省</th>\n",
       "      <td>2009</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃省</th>\n",
       "      <td>535</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西壮族自治区</th>\n",
       "      <td>1195</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江省</th>\n",
       "      <td>1677</td>\n",
       "      <td>86.0</td>\n",
       "      <td>5.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南省</th>\n",
       "      <td>678</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古自治区</th>\n",
       "      <td>567</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>128</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏回族自治区</th>\n",
       "      <td>202</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海省</th>\n",
       "      <td>68</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>202</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏自治区</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 60
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:20.234539Z",
     "start_time": "2024-09-26T03:35:20.217323Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 居住地省份的违约率计算\n",
    "province_current = get_badrate(df_Master,'UserInfo_7')\n",
    "province_current "
   ],
   "id": "1ae59d9f3d8c4507",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            total    bad  badrate\n",
       "UserInfo_7                       \n",
       "山东           2059  226.0    10.98\n",
       "天津            183   20.0    10.93\n",
       "四川           1117  108.0     9.67\n",
       "湖南           1056  100.0     9.47\n",
       "海南            164   15.0     9.15\n",
       "辽宁            557   48.0     8.62\n",
       "吉林            295   25.0     8.47\n",
       "江苏           1722  139.0     8.07\n",
       "湖北           1041   84.0     8.07\n",
       "不详           4187  333.0     7.95\n",
       "安徽            883   68.0     7.70\n",
       "河北            901   69.0     7.66\n",
       "重庆            457   34.0     7.44\n",
       "广东           3866  287.0     7.42\n",
       "江西            642   46.0     7.17\n",
       "上海             43    3.0     6.98\n",
       "黑龙江           430   30.0     6.98\n",
       "河南           1233   82.0     6.65\n",
       "陕西            523   32.0     6.12\n",
       "山西            778   47.0     6.04\n",
       "贵州            427   25.0     5.85\n",
       "福建           1880  105.0     5.59\n",
       "甘肃            287   15.0     5.23\n",
       "浙江           2182  113.0     5.18\n",
       "广西            848   43.0     5.07\n",
       "北京            639   32.0     5.01\n",
       "宁夏            164    8.0     4.88\n",
       "内蒙古           391   19.0     4.86\n",
       "青海             52    2.0     3.85\n",
       "云南            635   21.0     3.31\n",
       "新疆            179    3.0     1.68\n",
       "西藏             11    0.0     0.00"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>bad</th>\n",
       "      <th>badrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserInfo_7</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>山东</th>\n",
       "      <td>2059</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>183</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <td>1117</td>\n",
       "      <td>108.0</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南</th>\n",
       "      <td>1056</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南</th>\n",
       "      <td>164</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁</th>\n",
       "      <td>557</td>\n",
       "      <td>48.0</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>295</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏</th>\n",
       "      <td>1722</td>\n",
       "      <td>139.0</td>\n",
       "      <td>8.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北</th>\n",
       "      <td>1041</td>\n",
       "      <td>84.0</td>\n",
       "      <td>8.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>不详</th>\n",
       "      <td>4187</td>\n",
       "      <td>333.0</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽</th>\n",
       "      <td>883</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北</th>\n",
       "      <td>901</td>\n",
       "      <td>69.0</td>\n",
       "      <td>7.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆</th>\n",
       "      <td>457</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东</th>\n",
       "      <td>3866</td>\n",
       "      <td>287.0</td>\n",
       "      <td>7.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西</th>\n",
       "      <td>642</td>\n",
       "      <td>46.0</td>\n",
       "      <td>7.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>43</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江</th>\n",
       "      <td>430</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南</th>\n",
       "      <td>1233</td>\n",
       "      <td>82.0</td>\n",
       "      <td>6.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西</th>\n",
       "      <td>523</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西</th>\n",
       "      <td>778</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州</th>\n",
       "      <td>427</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建</th>\n",
       "      <td>1880</td>\n",
       "      <td>105.0</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃</th>\n",
       "      <td>287</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江</th>\n",
       "      <td>2182</td>\n",
       "      <td>113.0</td>\n",
       "      <td>5.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西</th>\n",
       "      <td>848</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>639</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏</th>\n",
       "      <td>164</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古</th>\n",
       "      <td>391</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海</th>\n",
       "      <td>52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>635</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆</th>\n",
       "      <td>179</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏</th>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:20.243815Z",
     "start_time": "2024-09-26T03:35:20.235532Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 各取前5名的省份进行二值化\n",
    "province_original.iloc[:5,]"
   ],
   "id": "8e1a044ae4eb05be",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             total    bad  badrate\n",
       "UserInfo_19                       \n",
       "天津市            137   17.0    12.41\n",
       "山东省           2366  256.0    10.82\n",
       "吉林省            498   47.0     9.44\n",
       "黑龙江省           813   71.0     8.73\n",
       "湖南省           1753  149.0     8.50"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>bad</th>\n",
       "      <th>badrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserInfo_19</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>137</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>2366</td>\n",
       "      <td>256.0</td>\n",
       "      <td>10.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>498</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>813</td>\n",
       "      <td>71.0</td>\n",
       "      <td>8.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>1753</td>\n",
       "      <td>149.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 62
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:20.256569Z",
     "start_time": "2024-09-26T03:35:20.244807Z"
    }
   },
   "cell_type": "code",
   "source": "province_current.iloc[:5,]",
   "id": "3c3d785aa8d4a14e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            total    bad  badrate\n",
       "UserInfo_7                       \n",
       "山东           2059  226.0    10.98\n",
       "天津            183   20.0    10.93\n",
       "四川           1117  108.0     9.67\n",
       "湖南           1056  100.0     9.47\n",
       "海南            164   15.0     9.15"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>bad</th>\n",
       "      <th>badrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserInfo_7</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>山东</th>\n",
       "      <td>2059</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>183</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <td>1117</td>\n",
       "      <td>108.0</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南</th>\n",
       "      <td>1056</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南</th>\n",
       "      <td>164</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 63
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:28.110290Z",
     "start_time": "2024-09-26T03:35:20.257560Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 分别对户籍省份和居住省份排名前五的省份进行二值化\n",
    "# 户籍省份的二值化\n",
    "df_Master['is_tianjin_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='天津市' else 0,axis=1)\n",
    "df_Master['is_shandong_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='山东省' else 0,axis=1)\n",
    "df_Master['is_jilin_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='吉林省' else 0,axis=1)\n",
    "df_Master['is_heilongjiang_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='黑龙江省' else 0,axis=1)\n",
    "df_Master['is_hunan_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='湖南省' else 0,axis=1)\n",
    " \n",
    "# 居住省份的二值化\n",
    "df_Master['is_tianjin_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='天津' else 0,axis=1)\n",
    "df_Master['is_shandong_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='山东' else 0,axis=1)\n",
    "df_Master['is_sichuan_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='四川' else 0,axis=1)\n",
    "df_Master['is_hainan_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='海南' else 0,axis=1)\n",
    "df_Master['is_hunan_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='湖南' else 0,axis=1)\n",
    " \n",
    " \n",
    "# 户籍省份和居住地省份不一致的特征衍生\n",
    "print(df_Master.UserInfo_19.unique())\n",
    "print('\\n')\n",
    "print(df_Master.UserInfo_7.unique())\n",
    " \n",
    " \n",
    "# 首先将两者改成相同的形式\n",
    "UserInfo_19_change = []\n",
    "for i in df_Master.UserInfo_19:\n",
    "    if i in ('内蒙古自治区','黑龙江省'):\n",
    "        j = i[:3]\n",
    "    else:\n",
    "        j = i[:2]\n",
    "    UserInfo_19_change.append(j)\n",
    "print(np.unique(UserInfo_19_change))\n",
    " \n",
    " \n",
    "# 判断UserInfo_7和UserInfo_19是否一致\n",
    "is_same_province=[]\n",
    "for i,j in zip(df_Master.UserInfo_7,UserInfo_19_change):\n",
    "    if i==j:\n",
    "        a=1\n",
    "    else:\n",
    "        a=0\n",
    "    is_same_province.append(a)\n",
    "df_Master['is_same_province'] = is_same_province"
   ],
   "id": "9ce1b494ea4b7625",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['四川省' '福建省' '湖北省' '江西省' '辽宁省' '山东省' '内蒙古自治区' '湖南省' '黑龙江省' '山西省' '江苏省'\n",
      " '云南省' '浙江省' '广东省' '天津市' '广西壮族自治区' '甘肃省' '贵州省' '陕西省' '重庆市' '河北省' '青海省'\n",
      " '安徽省' '上海市' '吉林省' '北京市' '河南省' '宁夏回族自治区' '新疆维吾尔自治区' '海南省' '西藏自治区']\n",
      "\n",
      "\n",
      "['广东' '浙江' '湖北' '福建' '辽宁' '不详' '内蒙古' '湖南' '黑龙江' '山西' '北京' '山东' '江苏' '云南'\n",
      " '天津' '广西' '重庆' '江西' '四川' '陕西' '贵州' '河北' '青海' '甘肃' '安徽' '吉林' '新疆' '海南'\n",
      " '河南' '宁夏' '上海' '西藏']\n",
      "['上海' '云南' '内蒙古' '北京' '吉林' '四川' '天津' '宁夏' '安徽' '山东' '山西' '广东' '广西' '新疆'\n",
      " '江苏' '江西' '河北' '河南' '浙江' '海南' '湖北' '湖南' '甘肃' '福建' '西藏' '贵州' '辽宁' '重庆'\n",
      " '陕西' '青海' '黑龙江']\n"
     ]
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:28.127301Z",
     "start_time": "2024-09-26T03:35:28.113304Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 2)城市特征\n",
    "# 原数据中有四个城市特征,推测为用户常登陆的IP地址城市\n",
    "# 特征衍生思路:\n",
    "# 一,通过xgboost挑选重要的城市,进行二值化\n",
    "# 二,由四个城市特征的非重复计数衍生生成登陆IP地址的变更次数\n",
    " \n",
    "# 根据xgboost变量重要性的输出对城市作二值化衍生\n",
    "df_Master_temp = df_Master[['UserInfo_2','UserInfo_4','UserInfo_8','UserInfo_20','target']]\n",
    "df_Master_temp.head()"
   ],
   "id": "887f61b4c0a70977",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  UserInfo_2 UserInfo_4 UserInfo_8 UserInfo_20  target\n",
       "0         深圳         深圳         深圳         南充市     0.0\n",
       "1         温州         温州         温州          不详     0.0\n",
       "2         宜昌         宜昌         宜昌         宜昌市     0.0\n",
       "3         南平         南平         南平          不详     0.0\n",
       "4         辽阳         辽阳         辽阳         锦州市     0.0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserInfo_2</th>\n",
       "      <th>UserInfo_4</th>\n",
       "      <th>UserInfo_8</th>\n",
       "      <th>UserInfo_20</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深圳</td>\n",
       "      <td>深圳</td>\n",
       "      <td>深圳</td>\n",
       "      <td>南充市</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>温州</td>\n",
       "      <td>温州</td>\n",
       "      <td>温州</td>\n",
       "      <td>不详</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>宜昌</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>宜昌市</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>南平</td>\n",
       "      <td>南平</td>\n",
       "      <td>南平</td>\n",
       "      <td>不详</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>辽阳</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>锦州市</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 65
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:28.968429Z",
     "start_time": "2024-09-26T03:35:28.129182Z"
    }
   },
   "cell_type": "code",
   "source": [
    "area_list=[]\n",
    "# 将四个城市特征都进行哑变量处理\n",
    "for col in df_Master_temp:\n",
    "    dummy_df = pd.get_dummies(df_Master_temp[col])\n",
    "    dummy_df = dummy_df.astype(int)\n",
    "    dummy_df = pd.concat([dummy_df,df_Master_temp['target']],axis=1)\n",
    "    area_list.append(dummy_df)\n",
    "df_area1 = area_list[0]\n",
    "df_area2 = area_list[1]\n",
    "df_area3 = area_list[2]\n",
    "df_area4 = area_list[3]\n",
    " \n",
    "df_area1"
   ],
   "id": "d3e9b334e85f0332",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       七台河  三明  三门峡  上海  上饶  东莞  东营  中山  临夏回族自治州  临汾  临沂  临沧  丹东  丽水  丽江  \\\n",
       "0        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "1        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "2        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "3        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "4        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "...    ...  ..  ...  ..  ..  ..  ..  ..      ...  ..  ..  ..  ..  ..  ..   \n",
       "49696    0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "49697    0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "49698    0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "49699    0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "49700    0   0    0   1   0   0   0   0        0   0   0   0   0   0   0   \n",
       "\n",
       "       乌兰察布盟  乌海  乌鲁木齐  乐山  九江  云浮  亳州  伊春  伊犁哈萨克自治州  佛山  佳木斯  保定  保山  信阳  \\\n",
       "0          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "1          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "2          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "3          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "4          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "...      ...  ..   ...  ..  ..  ..  ..  ..       ...  ..  ...  ..  ..  ..   \n",
       "49696      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "49697      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "49698      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "49699      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "49700      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "\n",
       "       克孜勒苏柯尔克孜自治州  克拉玛依  六安  六盘水  兰州  兴安盟  内江  凉山  包头  北京  北海  十堰  南京  南充  \\\n",
       "0                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "1                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "2                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "3                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "4                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "...            ...   ...  ..  ...  ..  ...  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "49696            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "49697            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "49698            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "49699            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "49700            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "       南宁  南平  南昌  南通  南阳  博尔塔拉蒙古自治州  厦门  双鸭山  台州  合肥  吉安  吉林市  吐鲁番  吕梁  吴忠  \\\n",
       "0       0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "1       0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "2       0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "3       0   1   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "4       0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "...    ..  ..  ..  ..  ..        ...  ..  ...  ..  ..  ..  ...  ...  ..  ..   \n",
       "49696   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "49697   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "49698   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "49699   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "49700   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "\n",
       "       周口  呼伦贝尔  呼和浩特  和田  咸宁  咸阳  哈密  哈尔滨  唐山  商丘  商洛  喀什  嘉兴  嘉峪关  四平  固原  \\\n",
       "0       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "1       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "2       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "3       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "4       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "...    ..   ...   ...  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ...  ..  ..   \n",
       "49696   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "49697   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "49698   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "49699   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "49700   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "\n",
       "       大兴安岭  大同  大庆  大理白族自治州  大连  天水  天津  太原  威海  娄底  孝感  宁德  宁波  安庆  安康  安阳  \\\n",
       "0         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "...     ...  ..  ..      ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "49696     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "49697     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "49698     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "49699     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "49700     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "       安顺  定西  宜宾  宜昌  宜春  宝鸡  宣城  宿州  宿迁  山南  岳阳  崇左  巢湖  巴中  巴彦淖尔盟  \\\n",
       "0       0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "1       0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "2       0   0   0   1   0   0   0   0   0   0   0   0   0   0      0   \n",
       "3       0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "4       0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..    ...   \n",
       "49696   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "49697   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "49698   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "49699   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "49700   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "\n",
       "       巴音郭楞蒙古自治州  常州  常德  平凉  平顶山  广元  广安  广州  庆阳  ...  珠海  甘南藏族自治州  甘孜藏族自治州  \\\n",
       "0              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "1              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "2              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "3              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "4              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "...          ...  ..  ..  ..  ...  ..  ..  ..  ..  ...  ..      ...      ...   \n",
       "49696          0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "49697          0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "49698          0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "49699          0   0   0   0    0   0   0   1   0  ...   0        0        0   \n",
       "49700          0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "\n",
       "       白城  白山  白银  百色  益阳  盐城  盘锦  眉山  石嘴山  石家庄  石河子  福州  秦皇岛  红河哈尼族彝族自治州  绍兴  \\\n",
       "0       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "1       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "2       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "3       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "4       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "...    ..  ..  ..  ..  ..  ..  ..  ..  ...  ...  ...  ..  ...         ...  ..   \n",
       "49696   0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "49697   0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "49698   0   0   0   0   0   0   0   0    0    0    0   0    1           0   0   \n",
       "49699   0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "49700   0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "\n",
       "       绥化  绵阳  聊城  肇庆  自贡  舟山  芜湖  苏州  茂名  荆州  荆门  莆田  莱芜  菏泽  萍乡  营口  葫芦岛  \\\n",
       "0       0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   \n",
       "1       0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   \n",
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       "3       0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   \n",
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       "      <th></th>\n",
       "      <th>七台河</th>\n",
       "      <th>三明</th>\n",
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       "      <th>克拉玛依</th>\n",
       "      <th>六安</th>\n",
       "      <th>六盘水</th>\n",
       "      <th>兰州</th>\n",
       "      <th>兴安盟</th>\n",
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       "      <th>大连</th>\n",
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       "    </tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49701 rows × 330 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 66
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:35.629604Z",
     "start_time": "2024-09-26T03:35:28.970420Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 使用xgboost筛选出重要的城市\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from xgboost import plot_importance\n",
    " \n",
    " \n",
    "# 注意,这里需要把合并后的没有目标标签的行数据删除\n",
    "# df_area1[~(df_area1['target'].isnull())]\n",
    " \n",
    " \n",
    "x_area1 = df_area1[~(df_area1['target'].isnull())].drop(['target'],axis=1)\n",
    "y_area1 = df_area1[~(df_area1['target'].isnull())]['target']\n",
    "x_area2 = df_area2[~(df_area2['target'].isnull())].drop(['target'],axis=1)\n",
    "y_area2 = df_area2[~(df_area2['target'].isnull())]['target']\n",
    "x_area3 = df_area3[~(df_area3['target'].isnull())].drop(['target'],axis=1)\n",
    "y_area3 = df_area3[~(df_area3['target'].isnull())]['target']\n",
    "x_area4 = df_area4[~(df_area4['target'].isnull())].drop(['target'],axis=1)\n",
    "y_area4 = df_area4[~(df_area4['target'].isnull())]['target']\n",
    " \n",
    " \n",
    " \n",
    "xg_area1 = XGBClassifier(random_state=0).fit(x_area1,y_area1)\n",
    "xg_area2 = XGBClassifier(random_state=0).fit(x_area2,y_area2)\n",
    "xg_area3 = XGBClassifier(random_state=0).fit(x_area3,y_area3)\n",
    "xg_area4 = XGBClassifier(random_state=0).fit(x_area4,y_area4)\n",
    " \n",
    " \n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "ax4 = fig.add_subplot(2,2,4)\n",
    " \n",
    "plot_importance(xg_area1,ax=ax1,max_num_features=10,height=0.4)\n",
    "plot_importance(xg_area2,ax=ax2,max_num_features=10,height=0.4)\n",
    "plot_importance(xg_area3,ax=ax3,max_num_features=10,height=0.4)\n",
    "plot_importance(xg_area4,ax=ax4,max_num_features=10,height=0.4)"
   ],
   "id": "95000ad55fbcd54e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 4 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 67
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:45.757850Z",
     "start_time": "2024-09-26T03:35:35.630600Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将特征重要性排名前三的城市进行二值化：\n",
    "df_Master['is_zibo_UserInfo_2'] = df_Master.apply(lambda x:1 if x.UserInfo_2=='淄博' else 0,axis=1)\n",
    "df_Master['is_chengdu_UserInfo_2'] = df_Master.apply(lambda x:1 if x.UserInfo_2=='成都' else 0,axis=1)\n",
    "df_Master['is_yantai_UserInfo_2'] = df_Master.apply(lambda x:1 if x.UserInfo_2=='烟台' else 0,axis=1)\n",
    " \n",
    "df_Master['is_zibo_UserInfo_4'] = df_Master.apply(lambda x:1 if x.UserInfo_4=='淄博' else 0,axis=1)\n",
    "df_Master['is_qingdao_UserInfo_4'] = df_Master.apply(lambda x:1 if x.UserInfo_4=='青岛' else 0,axis=1)\n",
    "df_Master['is_shantou_UserInfo_4'] = df_Master.apply(lambda x:1 if x.UserInfo_4=='汕头' else 0,axis=1)\n",
    " \n",
    "df_Master['is_zibo_UserInfo_8'] = df_Master.apply(lambda x:1 if x.UserInfo_8=='淄博' else 0,axis=1)\n",
    "df_Master['is_chengdu_UserInfo_8'] = df_Master.apply(lambda x:1 if x.UserInfo_8=='成都' else 0,axis=1)\n",
    "df_Master['is_heze_UserInfo_8'] = df_Master.apply(lambda x:1 if x.UserInfo_8=='菏泽' else 0,axis=1)\n",
    " \n",
    "df_Master['is_ziboshi_UserInfo_20'] = df_Master.apply(lambda x:1 if x.UserInfo_20=='淄博市' else 0,axis=1)\n",
    "df_Master['is_chengdushi_UserInfo_20'] = df_Master.apply(lambda x:1 if x.UserInfo_20=='成都市' else 0,axis=1)\n",
    "df_Master['is_sanmenxiashi_UserInfo_20'] = df_Master.apply(lambda x:1 if x.UserInfo_20=='三门峡市' else 0,axis=1)\n",
    " \n",
    " \n",
    "#特征衍生-IP地址变更次数特征\n",
    "df_Master['UserInfo_20'] = [a[:-1] if a.find('市')!= -1 else i[:] for a in df_Master.UserInfo_20]\n",
    "city_df = df_Master[['UserInfo_2','UserInfo_4','UserInfo_8','UserInfo_20']]\n",
    " \n",
    " \n",
    "city_change_cnt =[]\n",
    "for i in range(city_df.shape[0]):\n",
    "    a = list(city_df.iloc[i])\n",
    "    city_count = len(set(a))\n",
    "    city_change_cnt.append(city_count)\n",
    "df_Master['city_count_cnt'] = city_change_cnt\n",
    " \n",
    " \n",
    "# 3)运营商种类少,直接将其转换成哑变量\n",
    "print(df_Master.UserInfo_9.value_counts())\n",
    "print(set(df_Master.UserInfo_9))"
   ],
   "id": "f3f5e873c3344617",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UserInfo_9\n",
      "中国移动     25700\n",
      "中国联通      7389\n",
      "不详        6962\n",
      "中国电信      3287\n",
      "中国移动      2895\n",
      "中国电信      2052\n",
      "中国联通      1416\n",
      "Name: count, dtype: int64\n",
      "{'中国联通 ', '中国联通', '中国电信', '中国移动 ', '中国电信 ', '不详', '中国移动'}\n"
     ]
    }
   ],
   "execution_count": 68
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:45.880755Z",
     "start_time": "2024-09-26T03:35:45.759841Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_Master['UserInfo_9'] = df_Master.UserInfo_9.replace({'中国联通 ':'china_unicom',\n",
    "                              '中国联通':'china_unicom',\n",
    "                              '中国移动':'china_mobile',\n",
    "                              '中国移动 ':'china_mobile',\n",
    "                              '中国电信':'china_telecom',\n",
    "                              '中国电信 ':'china_telecom',\n",
    "                              '不详':'operator_unknown'\n",
    "    \n",
    "})\n",
    " \n",
    " \n",
    "operator_dummy = pd.get_dummies(df_Master.UserInfo_9)\n",
    "df_Master = pd.concat([df_Master,operator_dummy],axis=1)\n",
    " \n",
    "# 删除原变量\n",
    "df_Master = df_Master.drop(['UserInfo_9'],axis=1)\n",
    "df_Master = df_Master.drop(['UserInfo_19','UserInfo_2','UserInfo_4','UserInfo_7','UserInfo_8','UserInfo_20'],axis=1)\n",
    " \n",
    "# 看看还剩下哪些类型变量要处理\n",
    "df_Master.dtypes.value_counts()\n",
    "df_Master.select_dtypes(include='object')"
   ],
   "id": "c85dcee31d90ffe1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      WeblogInfo_19 WeblogInfo_20 WeblogInfo_21 ListingInfo sample_status\n",
       "0                 I            I5             D    2014/3/5         train\n",
       "1                 I            I5             D   2014/2/26         train\n",
       "2                 I            I5             D   2014/2/28         train\n",
       "3                 I            I5             D   2014/2/25         train\n",
       "4                 I           NaN             D   2014/2/27         train\n",
       "...             ...           ...           ...         ...           ...\n",
       "49696             I            I4             D   26/2/2014          test\n",
       "49697             I            I5             D   27/2/2014          test\n",
       "49698             I            I5             D  16/11/2013          test\n",
       "49699             I            I5             D   21/2/2014          test\n",
       "49700             I            I5             D   28/2/2014          test\n",
       "\n",
       "[49701 rows x 5 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WeblogInfo_19</th>\n",
       "      <th>WeblogInfo_20</th>\n",
       "      <th>WeblogInfo_21</th>\n",
       "      <th>ListingInfo</th>\n",
       "      <th>sample_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/3/5</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/26</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/28</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/25</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/27</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>49696</th>\n",
       "      <td>I</td>\n",
       "      <td>I4</td>\n",
       "      <td>D</td>\n",
       "      <td>26/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49697</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>27/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49698</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>16/11/2013</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49699</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>21/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49700</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>28/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49701 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 69
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:35:46.038579Z",
     "start_time": "2024-09-26T03:35:45.882749Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可以看到,我们要将这些weibo变量进行处理\n",
    "# 4) 微博特征\n",
    "for col in ['WeblogInfo_19','WeblogInfo_20','WeblogInfo_21']:\n",
    "    df_Master[col].replace({'nan':np.nan})   # 将字符型的nan,利用众数填充\n",
    "    df_Master[col] = df_Master[col].fillna(df_Master[col].mode()[0])\n",
    " \n",
    "# 看看这些变量有几种类型的值\n",
    "for col in ['WeblogInfo_19','WeblogInfo_20','WeblogInfo_21']:\n",
    "    print(df_Master[col].value_counts())\n",
    "    print('\\n')\n",
    " \n",
    "# 这里我们猜测WeblogInfo_20是WeblogInfo_19和21的更细化表达,这里直接删除该变量\n",
    "# 对其他变量进行哑变量处理\n",
    " \n",
    "df_Master['WeblogInfo_19'] = ['WeblogInfo_19'+ i for i in df_Master.WeblogInfo_19]\n",
    "df_Master['WeblogInfo_21'] = ['WeblogInfo_21'+ i for i in df_Master.WeblogInfo_21]\n",
    " \n",
    "for col in ['WeblogInfo_19','WeblogInfo_21']:\n",
    "    weibo_dummy = pd.get_dummies(df_Master[col])\n",
    "    df_Master = pd.concat([df_Master,weibo_dummy],axis=1)\n",
    "    \n",
    "# 删除原变量\n",
    "df_Master = df_Master.drop(['WeblogInfo_19','WeblogInfo_21','WeblogInfo_20'],axis=1)\n",
    " \n",
    "# 至此,类别变量处理完毕\n",
    "df_Master.dtypes.value_counts()"
   ],
   "id": "6270192961d6bba9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WeblogInfo_19\n",
      "I    41193\n",
      "D     5900\n",
      "F     1980\n",
      "E      314\n",
      "J      174\n",
      "G      135\n",
      "H        5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "WeblogInfo_20\n",
      "I5     31740\n",
      "I4      4122\n",
      "C21     4057\n",
      "U       2965\n",
      "I3      2756\n",
      "C19     1252\n",
      "C20      904\n",
      "C1       393\n",
      "C11      381\n",
      "F16      231\n",
      "C15      201\n",
      "C18      153\n",
      "C12       91\n",
      "C17       78\n",
      "F14       56\n",
      "F15       53\n",
      "F13       46\n",
      "I10       38\n",
      "I7        28\n",
      "C16       25\n",
      "O         21\n",
      "I6        20\n",
      "F11       19\n",
      "C13       18\n",
      "F12       13\n",
      "F10        9\n",
      "C38        7\n",
      "I8         6\n",
      "F9         5\n",
      "I11        3\n",
      "C39        2\n",
      "F3         2\n",
      "C14        1\n",
      "F7         1\n",
      "F6         1\n",
      "C32        1\n",
      "F5         1\n",
      "F2         1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "WeblogInfo_21\n",
      "D    40871\n",
      "A     6152\n",
      "C     2596\n",
      "B       82\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "float64    121\n",
       "int64       41\n",
       "bool        15\n",
       "object       2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 70
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:37:58.055515Z",
     "start_time": "2024-09-26T03:37:57.421391Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 我们来看看借款的成交时间趋势\n",
    "# 首先将字符型的日期转换成时间戳形式\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "df_Master['ListingInfo'] = pd.to_datetime(df_Master.ListingInfo, errors='coerce', format='%Y/%m/%d')\n",
    "# 删除 'ListingInfo' 列中值为 NaT 的行\n",
    "df_Master = df_Master.dropna(subset=['ListingInfo'])\n",
    "# df_Master['ListingInfo']\n",
    "df_Master[\"Month\"] = df_Master.ListingInfo.apply(lambda x:datetime.strftime(x,\"%Y-%m\"))\n",
    "df_Master[\"Month\"]\n",
    "\n",
    "plt.figure(figsize=(20,4))\n",
    "plt.title(\"借款成功的时间趋势变化\")\n",
    "plt.rcParams['font.sans-serif']=['Microsoft YaHei']\n",
    "sns.countplot(data=df_Master.sort_values('Month'),x='Month')\n",
    "plt.show()"
   ],
   "id": "1b53a1ceef9ad6b7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 73
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:38:30.422137Z",
     "start_time": "2024-09-26T03:38:30.173894Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 也可以看看违约率的月变化趋势 \n",
    "month_group = df_Master.groupby('Month')\n",
    "df_badrate_month = pd.DataFrame()\n",
    "df_badrate_month['total'] = month_group.target.count()\n",
    "df_badrate_month['bad'] = month_group.target.sum()\n",
    "df_badrate_month['badrate'] = df_badrate_month['bad']/df_badrate_month['total']\n",
    "df_badrate_month=df_badrate_month.reset_index()\n",
    " \n",
    " \n",
    "plt.figure(figsize=(12,4))\n",
    "plt.title('违约率的时间趋势图')\n",
    "sns.pointplot(data=df_badrate_month,x='Month',y='badrate',linestyles='-')\n",
    "plt.show()\n",
    "# 注:空值的部分代表的是预测样本"
   ],
   "id": "a3a4fd5560c598b0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 74
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:38:52.654261Z",
     "start_time": "2024-09-26T03:38:52.581446Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 我们不对数值型变量的缺失值做处理\n",
    "df_Master = df_Master.drop('Month',axis=1)\n",
    " \n",
    "# LogInfo表\n",
    "df_LogInfo\n",
    " \n",
    "# 衍生的变量有\n",
    "# 1)累计登陆次数\n",
    "# 2)登陆时间的平均间隔\n",
    "# 3)最近一次的登陆时间距离成交时间差\n",
    " \n",
    "# 1)累计登陆次数\n",
    "log_cnt = df_LogInfo.groupby('Idx',as_index=False).LogInfo3.count().rename(\n",
    "    columns={'LogInfo3':'log_cnt'})\n",
    "log_cnt.head(10)\n",
    " "
   ],
   "id": "adfe42cba0630594",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Idx  log_cnt\n",
       "0    3       26\n",
       "1    4       11\n",
       "2    5       11\n",
       "3    8      125\n",
       "4   11       30\n",
       "5   12      199\n",
       "6   13       16\n",
       "7   16       15\n",
       "8   17       11\n",
       "9   18       34"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 75
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:39:11.346534Z",
     "start_time": "2024-09-26T03:39:10.453766Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 2)最近一次的登陆时间距离成交时间差\n",
    " \n",
    "# 最近一次的登录时间距离当前时间差\n",
    "df_LogInfo['Listinginfo1']=pd.to_datetime(df_LogInfo.Listinginfo1)\n",
    "df_LogInfo['LogInfo3'] = pd.to_datetime(df_LogInfo.LogInfo3)\n",
    "time_log_span = df_LogInfo.groupby('Idx',as_index=False).agg({'Listinginfo1':np.max,\n",
    "                                                       'LogInfo3':np.max})\n",
    "time_log_span.head()\n",
    " \n",
    "time_log_span['log_timespan'] = time_log_span['Listinginfo1']-time_log_span['LogInfo3']\n",
    "time_log_span['log_timespan'] = time_log_span['log_timespan'].map(lambda x:str(x))\n",
    " \n",
    "time_log_span['log_timespan'] = time_log_span['log_timespan'].map(lambda x:int(x[:x.find('d')]))\n",
    "time_log_span= time_log_span[['Idx','log_timespan']]\n",
    "time_log_span.head()"
   ],
   "id": "18d63f44ce13fff2",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_8128\\3412248051.py:6: FutureWarning: The provided callable <function max at 0x00000272F47DCB80> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"max\" instead.\n",
      "  time_log_span = df_LogInfo.groupby('Idx',as_index=False).agg({'Listinginfo1':np.max,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   Idx  log_timespan\n",
       "0    3             4\n",
       "1    4             2\n",
       "2    5             1\n",
       "3    8             0\n",
       "4   11             0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>log_timespan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 76
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T07:34:39.340420Z",
     "start_time": "2024-09-26T07:34:32.647269Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 3)登陆时间的平均时间间隔\n",
    " \n",
    "df_temp_timeinterval = df_LogInfo.sort_values(by=['Idx','LogInfo3'],ascending=[True,True])\n",
    "df_temp_timeinterval = df_temp_timeinterval.groupby('Idx')['LogInfo3'].apply(lambda x:x.shift(1)).reset_index()\n",
    "# df_temp_timeinterval['LogInfo4'] = df_temp_timeinterval['LogInfo3']\n",
    "# df_temp_timeinterval\n",
    "df_temp_timeinterval"
   ],
   "id": "1e877720a442bf5f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          Idx  level_1   LogInfo3\n",
       "0           3   159876        NaT\n",
       "1           3   159877 2013-08-30\n",
       "2           3   159879 2013-08-30\n",
       "3           3   159880 2013-08-30\n",
       "4           3   159882 2013-08-30\n",
       "...       ...      ...        ...\n",
       "966426  91704   965152 2014-10-26\n",
       "966427  91704   965153 2014-10-26\n",
       "966428  91704   965154 2014-10-26\n",
       "966429  91704   965156 2014-10-26\n",
       "966430  91704   965155 2014-10-26\n",
       "\n",
       "[966431 rows x 3 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>level_1</th>\n",
       "      <th>LogInfo3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>159876</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>159877</td>\n",
       "      <td>2013-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>159879</td>\n",
       "      <td>2013-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>159880</td>\n",
       "      <td>2013-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>159882</td>\n",
       "      <td>2013-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966426</th>\n",
       "      <td>91704</td>\n",
       "      <td>965152</td>\n",
       "      <td>2014-10-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966427</th>\n",
       "      <td>91704</td>\n",
       "      <td>965153</td>\n",
       "      <td>2014-10-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966428</th>\n",
       "      <td>91704</td>\n",
       "      <td>965154</td>\n",
       "      <td>2014-10-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966429</th>\n",
       "      <td>91704</td>\n",
       "      <td>965156</td>\n",
       "      <td>2014-10-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966430</th>\n",
       "      <td>91704</td>\n",
       "      <td>965155</td>\n",
       "      <td>2014-10-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>966431 rows × 3 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 109
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:42:20.060320Z",
     "start_time": "2024-09-26T03:42:20.054421Z"
    }
   },
   "cell_type": "code",
   "source": "df_LogInfo.dtypes\n",
   "id": "82c49f575bca5254",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Idx                      int64\n",
       "Listinginfo1    datetime64[ns]\n",
       "LogInfo1                 int64\n",
       "LogInfo2                 int64\n",
       "LogInfo3        datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 80
  }
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